BackgroundMicroRNAs are a class of small non-coding RNAs that are involved in many important physiological and pathological processes by regulating gene expression negatively. The purpose of this study was to investigate the effect of miR-32 on cell proliferation, migration and apoptosis and to determine the functional connection between miR-32 and FBXW7 in breast cancer.MethodsIn this study, quantitative RT-PCR was used to evaluate the expression levels of miR-32 in 27 breast cancer tissues, adjacent normal breast tissues and human breast cancer cell lines. The biological functions of miR-32 in MCF-7 breast cancer cells were determined by cell proliferation, apoptosis assays and wound-healing assays. In addition, the regulation of FBXW7 by miR-32 was assessed by qRT-PCR, Western blot and luciferase reporter assays.ResultsMiR-32 was frequently overexpressed in breast cancer tissue samples and cell lines as was demonstrated by qRT-PCR. Moreover, the up-regulation of miR-32 suppressed apoptosis and promoted proliferation and migration, whereas down-regulation of miR-32 showed an opposite effect. Dual-luciferase reporter assays showed that miR-32 binds to the 3′-untranslated region of FBXW7, suggesting that FBXW7 is a direct target of miR-32. Western blot analysis showed that over-expression of miR-32 reduced FBXW7 protein level. Furthermore, an inverse correlation was found between the expressions of miR-32 and FBXW7 mRNA levels in breast cancer tissues. Knockdown of FBXW7 promoted proliferation and motility and suppressed apoptosis in MCF-7 cells.ConclusionsTaken together, the present study suggests that miR-32 promotes proliferation and motility and suppresses apoptosis of breast cancer cells through targeting FBXW7.
2633 Background: Emerging data suggest that concomitant medications (CM) influence response to ICI. CM impact the host microbiome which may mitigate tumor-immune responsiveness. PPI use in patients treated with ICI has been associated with worse survival. Few data exist regarding the effects of PPI use in terms of prior chemotherapy or in risk for immune related adverse events (irAE) (e.g., colitis). Methods: This retrospective study of patients with advanced cancer treated with ICI between 2011 and 2019 was conducted at The Ohio State University. Patients who received ICI as either single agent or combination were included. Clinical data was abstracted from chart review, including CM, toxicity, and survival. Overall survival (OS) was evaluated to date of death or last contact. Associations between OS and proton pump inhibitor (PPI) use were studied using log-rank tests and Cox regression analyses overall and by the groups of whether prior chemotherapy was administered and timing from chemotherapy to ICI. The associations between PPI and incidence of irAE (overall and colitis) were assessed by chi-square tests. Results: We identified 1,091 patients treated with ICI, of whom 415 (38%) received PPI at time of ICI. Most common cancers were NSCLC and melanoma; most common therapy was PD1/L1 (Table). PPI use was associated with shorter OS in patients treated as first line therapy (HR = 1.46, 95% CI = [1.11, 1.91], p=0.006) and in second line and beyond (HR = 1.30, 95% CI = [1.10, 1.53], p=0.002). PPI use was associated with shorter OS in patients treated with ICI for those without prior chemotherapy (HR = 1.47, 95% CI = [1.17, 1.86], p=0.001). When evaluated by timing from chemotherapy to ICI, PPI use was associated with shorter OS only in patients where last chemotherapy was > 1 year from ICI (HR = 1.99, 95% CI [1.15, 3.45], p=0.014) but not for patients with chemotherapy within 1 year of ICI (HR = 1.01, 95% CI = [0.79, 1.29], p=0.960). The use of PPI was not associated with incidence of irAE (p=0.317) or colitis in particular (p=0.781). Conclusions: PPI use was associated with shorter survival in patients treated with ICI across a broad variety of cancers and in first line of therapy or beyond. In patients with recent chemotherapy (<1 year), PPI use was not associated with survival, which may be due to disruption of the microbiome by chemotherapy. Further study is needed to determine the impact of CM (e.g, PPI), on outcomes of patients treated with ICI.[Table: see text]
The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is unclear. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool to carefully identify the tumor microbiome within RNAseq datasets and then applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas (TCGA). We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We identify associations with survival and clinical variables that are highly cancer-specific, and relatively few associations with immune composition. Finally, we explore potential mechanisms by which microbes and tumors may interact using a network approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The exotic tool can support discovery of microbes in tumors in a way that leverages the many existing and growing number of RNAseq datasets.
1311 Figure 1 A stacked bar plot showing the relative abundances of exogenous taxa found in tumor RNAseq. Taxa are shown on the phylum level and are ordered by the relative abundance of < i >Uroviricota Abstract 1311 Figure 2 Differential abundance analysis of taxa found within tumor RNAseq data by the exotic pipeline. Colored points represent significantly (p-value < 0.05) enriched taxa with a high (>1.00) fold-difference in abundance between the groups
11541 Background: Sarcoma is a heterogeneous group of malignant tumors that consist of distinct histological and molecular subtypes, each with unique features. Despite immunotherapy’s promise in many cancers, immunotherapeutic approaches for sarcoma have had variable response rates. Evaluating the tumor microbiome is a promising new approach that aims to improve our understanding of the immunogenicity of sarcoma subtypes, leading to improved treatment options and better clinical outcomes. Methods: We utilized The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) database to obtain RNA sequencing (RNAseq) data to identify microbes in sarcoma samples (all subtypes available). Due to the large number of sarcoma subtypes, we focused on three groups: dedifferentiated liposarcoma (DDLPS), leiomyosarcoma (LMS) and “other,” representing all other sarcoma subtypes. We utilized ExoTIC, “Exogenous sequences in Tumors and Immune cells,” a tool recently developed by Dr. Daniel Spakowicz and Dr. Xaiokui Mo. ExoTIC takes raw RNAseq reads and carefully aligns to both human and non-human reference genomes to identify low-abundance microbes. Models of association were analyzed based on each of the three groups as well as all the samples: “All” group. We performed Cox proportional hazards regression to identify the microbes associated with overall survival (OS). Results: We evaluated 97 LMS, 56 DDLPS and 100 “other” RNAseq samples (Table). ExoTIC identified 1304 microbes, of which 431 were statistically associated with OS in the “All” group. Of these, 50 microbes were statistically associated only with DDLPS, 54 only with LMS (e.g., Candida dubliniensis, Mycobacterium avium, Streptococcus sp. Z15), and 46 with “other.” The presence of no organism was associated with improved survival. Median hazard ratios were largest in DDLPS (2.3), followed by “other” (2.1) and LMS (1.9). Only 18 microbes were found in the DDLPS, LMS and “All” groups, including Bacillus sp., Streptococcus lutetiensis, Clostridium tetani, and Pseudomonas sp. LTJR-52. Each was negatively correlated with survival with a median hazard ratio of 2.5. Conclusions: We found a specific relationship between microbial presence and histological sarcoma subtype (DDLPS, LMS), which also statistically correlated with OS. Assessing individual characteristics of a sarcoma histological subtype with its particular microenvironment (e.g., microbes) can lead to personalized treatment insights and improvements in outcomes. Our future research will consist of validating and correlation of the microbial profile of sarcoma subtypes with clinical outcomes retrospectively and prospectively. [Table: see text]
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